Development of clusterwise methods for high dimensional symbolic data and its applications
| Project/Area Number |
23500343
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Statistical science
|
|---|
| Research Institution | University of Tsukuba |
|---|
Principal Investigator |
SATO-ILIC Mika 筑波大学, システム情報系, 教授 (60269214)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
AOSHIMA Makoto 筑波大学, 数理物質系, 教授 (90246679)
SHIMIZU Nobuo 統計数理研究所, サービス科学研究センター, 助教 (00332130)
|
|---|
| Co-Investigator(Renkei-kenkyūsha) |
TANAKA Kazuo 電気通信大学, 情報工学研究科, 教授 (00227125)
|
|---|
| Project Period (FY) |
2011 – 2013
|
| Project Status |
Completed (Fiscal Year 2013)
|
| Budget Amount *help |
¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2011: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
|
|---|
| Keywords | 分類 / パターン認識 / シンボリックデータ / 高次元小標本データ / クラスタリング / データマイニング / ファジィクラスタリング / 類似性 / クラスター間相関 / 遺伝子データ / シンボリックデータ解析 / 主成分分析 |
|---|
| Research Abstract |
A problem with conventional statistical analyses for high dimension low sample-size data is that an efficient solution will not be obtained. Methods to solve this problem for symbolic data have not been proposed. Therefore, this research develops methods for high dimension low sample-size symbolic data and proposes a new knowledge discovery method considering a variety of data with an evaluation of the performance of this method.
|
Report
(4 results)
Research Products
(45 results)
